Traditional natural language processing systems include tagging systems and dialogue managers. The tagging systems typically classify each word in a sentence using hard syntactic part of speech classification. This results in the classification of each word, for example, as a noun, verb, adverb, adjective, article, preposition or conjunction. However, the tagging systems do not provide any information about the relatedness between the words, where such information may provide context about the meaning of the word within the sentence or how the word may be used to complete a task.
The dialogue manager typically includes functionality to guide the user to provide appropriate inputs in order to complete a task. Because the traditional tagging systems do not provide any context about what a word means within a sentence and, instead, only provide to which part of speech each word in the sentence corresponds, the dialogue manager (or another component in the natural language processing system) must include additional functionality to determine what each of the words in the sentences means in order to present the appropriate dialogue to the user. The disconnect between the tagging system and the dialogue manager may result in inefficient generation of dialogue and ultimately inefficient task completion.